Embodiments of the subject matter disclosed herein generally relate to methods and systems and, more particularly, to mechanisms and techniques for predicting and optimizing the operating life of a gas turbine, or components therein.
FIG. 1, which is similar to FIG. 1 of U.S. Patent Application Publication 2008/0243352 (incorporated herein by reference), illustrates an example of a gas turbine 10 having a compressor 12, a combustor 14, a turbine 16 coupled to the compressor 12, and a computer control system (controller) 18. An inlet duct 20 to the compressor 12 may feed ambient air to compressor 12. The inlet duct 20 may have ducts, filters, screens and noise abatement devices that contribute to a pressure loss of ambient air flowing through the inlet 20 and into inlet guide vanes 21 of the compressor 12. An exhaust duct 22 for the turbine directs combustion gases from the outlet of the turbine 10 through, for example, emission control and noise abatement devices. The turbine 10 may drive a generator 24 that produces electrical power. Alternatively, whenever the turbine is a two shaft device (e.g., including a high pressure turbine and a low pressure turbine), the low pressure turbine, which can turn at a different speed than the high pressure rotor, may drive a more generic machine as a compressor or even a generator.
The operation of the gas turbine 10 may be monitored by several sensors 26 designed to measure different performance-related variables of the turbine 10, the generator and the ambient environment. For example, groups of redundant temperature sensors 26 may monitor ambient temperature surrounding the gas turbine 10, compressor discharge temperature, turbine exhaust gas temperature, and other temperature measurements of the gas stream through the gas turbine 10. Similarly, groups of redundant pressure sensors 26 may monitor ambient pressure, and static and dynamic pressure levels at the compressor inlet and outlet turbine exhaust, at other locations in the gas stream through the gas turbine 10. Groups of redundant humidity sensors 26, for example, wet and dry bulb thermometers, may measure ambient humidity in the inlet duct of the compressor 12. Groups of redundant sensors 26 may also include flow sensors, speed sensors, flame detector sensors, valve position sensors, guide vane angle sensors, or the like, that sense various parameters pertinent to the operation of gas turbine 10. As used herein, “parameters” refer to items that can be used to define the operating conditions of the turbine, such as, but not limited to, temperatures, pressures, and gas flows at defined locations in the turbine.
Also, the fuel control system 28 regulates the fuel flowing from a fuel supply to the combustor 14, one or more splits between the fuel flowing into primary and secondary fuel nozzles, and the amount of fuel mixed with secondary air flowing into a combustion chamber. The fuel control system 28 may also select the type of fuel for the combustor. The fuel control system 28 may be a separate unit or may be a component of the main controller 18. The controller 18 may be a computer system having at least one processor that executes programs and operations to control the operation of the gas turbine using sensor inputs and instructions from human operators. The commands generated by the controller 18 may cause actuators on the gas turbine to, for example, adjust valves (actuator 27) between the fuel supply and combustors that regulate the flow, fuel splits and type of fuel flowing to the combustors, adjust inlet guide vanes 21 (actuator 29) on the compressor, adjust inlet bleed heat, as well as activate other control settings on the gas turbine.
The turbine may have a wide application in the oil and gas field. That is, it may drive compressors in the pipelines as well it may still drive compressors to pump out petrol or natural gas from wells. An important critical quality (CTQ) for oil and gas companies is the availability of their plants so to maximize production. To minimize plant shut down or disruption, plant core parts, such as the gas turbine, are ideally replaced/maintained just when their probability of failure has a substantial impact on plant reliability. Another important CTQ is maintenance cost, which should be minimized wherever possible.
To improve these and other CTQs, various entities have launched life extension initiatives as CBM (condition based maintenance) and RLM (rotor life management). Some techniques rely solely on optical inspections to measure crack lengths to develop a statistical distribution of crack lengths. This statistical distribution is used to estimate equipment life expectancy. Another technique involves inspecting a component for damage or deterioration (e.g., micro-cracks), forming a structural model of the device, setting a future use condition for the device, and simulating an advancement of the damage or deterioration. Another approach is to estimate creep damage via an expression including a Larson-Miller expression, and then performing statistical analysis (e.g., Weibull statistical analysis) to estimate future creep damage. Here, an estimation parameter based on an equipment start count and thermal stress is calculated based on a statistical model. Another approach is to determine a relationship between a metal temperature of a turbine component and an operating condition of the turbine housing the component. This approach uses a thermal model of the component and an operating history of the turbine to predict a current or future component operating temperature.
However, the conventional methods and systems for plant and turbine life extension require plant shut down for inspections to collect data (e.g., crack lengths) and in general are not applicable for components that do not show evident failure. Accordingly, it would be desirable to provide systems and methods that avoid the afore-described problems and drawbacks.